Methods and apparatuses for determining face image quality, electronic devices, and computer storage media

ABSTRACT

A method for determining face image quality includes: obtaining pose angle information and/or size information of a face in an image; and obtaining quality information of the face in the image on the basis of the pose angle information and/or the size information of the face.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 16/655,235filed on Oct. 17, 2019, which is a continuation of InternationalApplication No. PCT/CN2018/087915 filed on May 22, 2018, which claimspriority to Chinese Patent Application No. 201710405232.9 filed May 31,2017. The disclosures of the above-referenced applications areincorporated herein by reference in their entirety.

BACKGROUND

With the development of computer vision technologies, face recognitiontechnologies have a great improvement in performance in recent years.Face recognition in non-extreme scenes may reach a level close to thatof artificial recognition. Face recognition technologies are more widelyapplied to various scenes in life.

SUMMARY

The present disclosure relates to computer vision technologies, and inparticular, to methods and apparatuses for determining face imagequality, electronic devices, and computer storage media.

Embodiments of the present disclosure provide technical solutions fordetermining face image quality.

A method for determining face image quality provided according to oneaspect of the embodiments of the present disclosure includes:

obtaining at least one of pose angle information of a face in an imageor size information of the face; and

obtaining quality information of the face in the image on the basis ofat least one of the pose angle information of the face or the sizeinformation of the face.

An apparatus for determining face image quality provided according toanother aspect of the embodiments of the present disclosure includes:

a first obtaining module, configured to obtain at least one of poseangle information of a face in an image or size information of the face;and

a second obtaining module, configured to obtain quality information ofthe face in the image on the basis of at least one of the pose angleinformation of the face or the size information of the face.

An electronic device provided according to still another aspect of theembodiments of the present disclosure includes the apparatus fordetermining face image quality according to any one of the foregoingembodiments of the present disclosure.

Another electronic device provided according to still another aspect ofthe embodiments of the present disclosure includes a processor and amemory for storing instructions executable by the processor; whereexecution of the instructions by the processor causes the processor toperform: obtaining at least one of pose angle information of a face inan image or size information of the face; and obtaining qualityinformation of the face in the image on the basis of at least one of thepose angle information of the face or the size information of the face.

A non-transitory computer storage medium provided according to yetanother aspect of the embodiments of the present disclosure isconfigured to store computer-readable instructions, where execution ofthe instructions by the processor causes the processor to perform:obtaining at least one of pose angle information of a face in an imageor size information of the face; and obtaining quality information ofthe face in the image on the basis of at least one of the pose angleinformation of the face or the size information of the face.

A computer program provided according to yet another aspect of theembodiments of the present disclosure, including a computer instruction,where when the computer instruction runs in a processor of a device, theprocessor executes operations corresponding to method for determiningface image quality according to any one of the foregoing embodiments ofthe present application.

On the basis of methods and apparatuses for determining face imagequality, the electronic devices, and the computer storage media providedaccording to the foregoing embodiments of the present disclosure, poseangle information and/or size information of a face in an image areobtained, and quality of the face in the image is obtained on the basisof the pose angle information and/or the size information of the face.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings constituting a part of the specificationdescribe the embodiments of the present disclosure and are intended toexplain the principles of the present disclosure together with thedescriptions.

According to the following detailed descriptions, the present disclosuremay be understood more clearly with reference to the accompanyingdrawings.

FIG. 1 illustrates a flowchart of one embodiment of a method fordetermining face image quality according to the present disclosure.

FIG. 2 illustrates a flowchart of another embodiment of a method fordetermining face image quality according to the present disclosure.

FIG. 3 illustrates a flowchart of still another embodiment of a methodfor determining face image quality according to the present disclosure.

FIG. 4 illustrates a flowchart of one specific application embodiment ofa method for determining face image quality according to the presentdisclosure.

FIG. 5 illustrates a schematic structural diagram of one embodiment ofan apparatus for determining face image quality according to the presentdisclosure.

FIG. 6 illustrates a schematic structural diagram of another embodimentof an apparatus for determining face image quality according to thepresent disclosure.

FIG. 7 illustrates a schematic structural diagram of still anotherembodiment of an apparatus for determining face image quality accordingto the present disclosure.

FIG. 8 illustrates a schematic structural diagram of one embodiment ofan electronic device of the present disclosure.

DETAILED DESCRIPTION

Various exemplary embodiments of the present disclosure are nowdescribed in detail with reference to the accompanying drawings. Itshould be noted that, unless otherwise stated specifically, relativearrangement of the components and operations, the numerical expressions,and the values set forth in the embodiments are not intended to limitthe scope of the present disclosure.

In addition, it should be understood that, for ease of description, thesize of each part shown in the accompanying drawings is not drawn inactual proportion.

The following descriptions of at least one exemplary embodiment aremerely illustrative actually, and are not intended to limit the presentdisclosure and the applications or uses thereof.

Technologies, methods and devices known to a person of ordinary skill inthe related art may not be discussed in detail, but such technologies,methods and devices should be considered as a part of the specificationin appropriate situations.

It should be noted that similar reference numerals and letters in thefollowing accompanying drawings represent similar items. Therefore, oncean item is defined in an accompanying drawing, the item does not need tobe further discussed in the subsequent accompanying drawings.

The embodiments of the present disclosure may be applied to electronicdevices such as terminal devices, computer systems, and servers, whichmay operate with numerous other general-purpose or special-purposecomputing system environments or configurations. Examples of well-knownterminal devices, computing systems, environments, and/or configurationssuitable for use together with the electronic devices such as terminaldevices, computer systems, and servers include, but are not limited to,Personal Computer (PC) systems, server computer systems, thin clients,thick clients, handheld or laptop devices, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, distributed cloudcomputing environments that include any one of the foregoing systems.

The electronic devices such as terminal devices, computer systems, andservers may be described in the general context of computersystem-executable instructions (for example, program modules) executedby the computer system. Generally, the program modules may includeroutines, programs, target programs, components, logics, datastructures, and the like for performing specific tasks or implementingspecific abstract data types. The computer systems/servers may beimplemented in the distributed cloud computing environments in whichtasks are performed by remote processing devices linked via acommunication network. In the distributed computing environments, theprogram modules may be located in local or remote computing systemstorage media including storage devices.

FIG. 1 is a flowchart of one embodiment of a method for determining faceimage quality according to the present disclosure. As shown in FIG. 1,the method for determining face image quality of this embodimentincludes:

102: pose angle information and/or size information of a face in animage are obtained.

A pose angle of the face is a head pose angle of human, including a yawangle and a pitch angle of the face in head normalized sphericalcoordinates (i.e., an image acquisition coordinate system), where theyaw angle is used for indicating a side face angle of the face in ahorizontal direction, and the pitch angle is used for indicatinghead-down or head-up angle of the face in a vertical direction. Underthe condition that the face size is fixed, the smaller the yaw angle andthe pitch angle are, the more front-facing the face is, the easier theface recognition is, and the higher the face recognition accuracy is.When both the yaw angle and the pitch angle are zero, the face is themost front-facing and the face recognition accuracy is the highest.

The face size is also a face pixel size, and the bigger the face is, thehigher the definition is, the easier the face recognition is, and thehigher the face recognition accuracy is.

104: Quality information of the face in the image is obtained on thebasis of the pose angle information and/or the size information of theface.

The higher the quality of the face in the image is, the better thequality of the face in the image is; on the contrary, the lower thequality of the face in the image is, the worse the quality of the facein the image is.

At present, false recognition may easily occur in a face recognitionprocess. In addition to a recognition algorithm model, the falserecognition rate is also related to the quality of the face image. Ifthe quality of face image is poor, for example, the side face angle isexcessively large and the face pixel is excessively small, the facerecognition accuracy generally decreases, and the false recognition rateis relatively high. In actual scenes, most of false recognition andmissing recognition are caused due to the fact that the quality of theface image is not high enough. Therefore, the method for determiningface image quality which draws enough attention is helpful for improvingthe face recognition rate and is very important.

The existing image quality evaluation methods may be divided into twocategories: subjective evaluation methods and objective evaluationmethods. With the improvement of the automation level, the subjectiveevaluation methods requiring manual participation in many fields have alot of inconvenience, high costs and long cycles, and therefore theobjective evaluation methods are gradually developed. Currently, themethod for determining face image quality has not attracted enoughattention, the objective evaluation method for face image quality is notyet mature, and the evaluation result for face image quality isinaccurate enough.

In order to evaluate the face image quality, face image qualityevaluation indexes need to be established, and evaluation criteria forgood face image quality need to be defined. In order to improve the facerecognition rate, defining evaluation criteria for good face imagequality should enable the face to be easily recognized, for example, theface is easy to be recognized when needing to meet conditions such ashigh definition, big face, and front-facing face. In actual applicationscenes, the definition of the face image is influenced by two aspects:one is that the image captured by a camera is blurred, and the other isthat the face image itself is excessively small. Since the size of theface image needs to be uniformly scaled to a standard size before faceimage recognition, when a small face image is amplified to the standardsize, fuzziness caused by pixel interpolation may exist. In general,after a proper camera is selected according to the application scene,the image captured thereby is clear. Therefore, disregarding the casethat the image captured by the camera is not sharp, the definition ofthe face image and the face size are positively related. The bigger theface is, the higher the definition is. The face definition may beevaluated by using the face size.

According to the method for determining face image quality in theembodiments of the present disclosure, from the perspective offacilitating face recognition, the face image quality is evaluated onthe basis of key factors affecting the face recognition result (forexample, face definition, face size, and whether the face isfront-facing), and indexes for evaluating the key factors affecting theface recognition result are obtained: a pose angle of the face and aface size. The front-facing degree of the face is determined based onthe pose angle of the face, the face definition is determined based onthe face size, and the face image quality is evaluated according to thepose angle information and the size information of the face. Accordingto the technical solutions for determining face image quality in theembodiments of the present disclosure, the face image quality isobjectively evaluated, and the accuracy rate of the evaluation result ofthe face image quality is high; in addition, according to theembodiments of the present disclosure, by obtaining the size informationof the face to reflect the face definition affecting the facerecognition result instead of directly obtaining the face definition inthe image, as compared with directly obtaining the face definition inthe image, the method improves the operation efficiency and increasesthe real-time performance of the face quality evaluation.

According to one or more embodiments of the present disclosure, inoperation 102, the obtaining the pose angle information of the face inthe image may be implemented in the following mode:

a face detection bounding box in the image and key point coordinates ofthe face determined according to the face detection bounding box areobtained. For example, face detection may be performed on the image toobtain the face detection bounding box, and key point (for example,corners of eyes and mouth) positioning is performed on the face in theface detection bounding box to obtain key point coordinates of the face;the pose angle information of the face is obtained according to the keypoint coordinates of the face, where the pose angle information of theface includes a yaw angle and a pitch angle of the face.

According to one or more embodiments of the present disclosure, inoperation 102, the obtaining the size information of the face in theimage may be implemented in the following mode: obtaining the sizeinformation of the face according to the size of the face detectionbounding box, where the size of the face detection bounding box includeslength and/or width of the face detection bounding box.

The technical solutions provided according to the embodiments of thepresent disclosure include the following beneficial effects:

Through the method for evaluating face image quality based on keyfactors affecting a face recognition result (face definition, face size,and whether the face is front-facing), indexes for evaluating the keyfactors affecting the face recognition result are obtained: a pose angleof the face for reflecting whether the face is front-facing, and facesize for reflecting face definition and face size; and a method forevaluating face image quality according to pose angle information andsize information of a face is obtained. According to the technicalsolutions for determining face image quality in the embodiments of thepresent disclosure, the face image quality may be objectively evaluated,and the accuracy rate of the evaluation result of the face image qualityis high; in addition, in the embodiments of the present disclosure, byobtaining the size information of the face to reflect the facedefinition affecting the face recognition result instead of directlyobtaining the face definition in the image, as compared with directlyobtaining the face definition in the image, the method facilitatesimproving the operation efficiency and increasing the real-timeperformance of face quality evaluation.

It should be understood that the above general description and thefollowing detailed description are merely exemplary and explanatory andare not intended to limit the present disclosure. The following furtherdescribes in detail the technical solutions of the present disclosurewith reference to the accompanying drawings and embodiments.

According to one or more embodiments of the present disclosure,operation 104 may include: obtaining the score of the pose angle of theface according to the pose angle information of the face; obtaining thescore of the face size according to the size information of the face;and obtaining a quality score of the face in the image according to thescore of the pose angle of the face and the score of the face size.

FIG. 2 is a flowchart of another embodiment of a method for determiningface image quality according to the present disclosure. As shown in FIG.2, the method for determining face image quality of this embodimentincludes:

202: a face detection bounding box in the image and key pointcoordinates of the face determined according to the face detectionbounding box are obtained.

The face detection bounding box includes a face image detected from theimage.

For example, face detection may be performed on the image by means of aface detection algorithm to obtain the face detection bounding box;

for example, key point positioning is performed on the face in the facedetection bounding box by means of a key point detection algorithm toobtain the key point coordinates of the face.

204: The pose angle information of the face is obtained according to thekey point coordinates of the face, and the size information of the faceis obtained according to the size of the face detection bounding box.

The size of the face detection bounding box includes length and/or widthof the face detection bounding box. In a specific example, the size ofthe face detection bounding box is the face size. A pose angle of theface is a head pose angle of the human, including a yaw angle and apitch angle of the face in head normalized spherical coordinates (i.e.,an image acquisition coordinate system), where the yaw angle is used forindicating the angle of the side face of the face in a horizontaldirection, and the pitch angle is used for indicating the head-down orhead-up angle of the face in a vertical direction. Under the conditionthat the face size is fixed, the smaller the yaw angle and the pitchangle are, the more front-facing the face is, the easier the facerecognition is, and the higher the face recognition accuracy is. Whenboth the yaw angle and the pitch angle are zero, the face is the mostfront-facing and the face recognition accuracy is the highest.

The face size is also the face pixel size, and the bigger the face is,the higher the definition is, the easier the face recognition is, andthe higher the face recognition accuracy is.

206: The score of the pose angle of the face is obtained according tothe pose angle information of the face, and the score of the face sizeis obtained according to the size information of the face.

According to one or more embodiments of the present disclosure, thescore of the pose angle of the face may be obtained in the followingmode: according to the yaw angle and the pitch angle of the face,obtaining the score Q_(yaw) of the yaw angle yaw of the face bycalculation based on

$Q_{yaw} = e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}$

and obtaining the score Q _(pitch) of the pitch angle (“pitch”) of theface by calculation based on

$Q_{pitch} = e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}$

According to one or more embodiments of the present disclosure, thescore of the face size may be obtained in the following mode: obtainingthe score of the face size on the basis of at least one of length,width, or area of the face detection bounding box, where the area of theface detection bounding box is obtained by the product of the length andthe width of face detection bounding box. The length, width, and area offace detection bounding box correspond to the size of the face image.Therefore, the score of the face size may be determined on the basis ofat least one of length, width, or area of the face detection boundingbox.

Further exemplarily, the obtaining the score of the face size on thebasis of at least one of length, width, or area of the face detectionbounding box, for example, may be: selecting a smaller value min in thelength and the width of the face detection bounding box;

and obtaining the score Q_(rect) of the face size by calculation basedon

$Q_{rect} = \frac{1}{1 + e^{\frac{{- 2^{*}}{({\min - 50})}}{75}}}$

according to the smaller value min in the length and the width.

The face size may be better determined by means of the smaller value inthe length and the width of the face detection bounding box, so that thescore of the face size is obtained by calculation on the basis of thesmaller value in the length and the width of the face detection boundingbox, and the face size may be reflected more objectively.

208: The quality score of the face in the image is obtained according tothe score of the pose angle of the face and the score of the face size.

The higher the quality score of the face in the image is, the better thequality of the face in the image is; on the contrary, the lower thequality score of the face in the image is, the worse the quality of theface in the image is.

According to one or more embodiments of the present disclosure,operation 208 may be implemented in the following mode:

The quality score of the face in the image is obtained by calculationaccording to the score of the yaw angle and its weight, the score of thepitch angle and its weight, and the score of the face size and itsweight.

The weight of the score of the yaw angle, the weight of the score of thepitch angle, and the weight of the score of the face size may be preset,and may be adjusted according to actual requirements. In general, theyaw angle has the greatest influence on the accuracy of the facerecognition result. In a specific application, the weight of the scoreof the yaw angle may be set to be greater than the weight of the scoreof the pitch angle and the weight of the score of the size of the face,so that the obtained quality score of the face in the image may moreaccurately and objectively reflect the quality of the face in one image.

According to the method for determining face image quality in theembodiments of the present disclosure, from the perspective offacilitating face recognition, the face image quality is evaluated onthe basis of key factors affecting the face recognition result (facedefinition, face size, and whether the face is front-facing), andindexes for evaluating the key factors affecting the face recognitionresult are obtained: a pose angle of the face and a face size. Thefront-facing degree of the face is determined based on the pose angle ofthe face, the face definition is determined based on the face size, thescore of the pose angle of the face and the score of the face size arefurther obtained. The quality score of the face in the image is obtainedaccording to the score of the pose angle of the face and the score ofthe face size, so as to more accurately and objectively evaluate thequality of the face in the image, and the accuracy rate of theevaluation result of the face image quality is high; in addition,according to the embodiments of the present disclosure, by obtaining thesize information of the face to reflect the face definition affectingthe face recognition result instead of directly obtaining the facedefinition in the image, as compared with the directly obtaining theface definition in the image, the method improves the operationefficiency and increases the real-time performance of the face qualityevaluation.

FIG. 3 is a flowchart of still another embodiment of a method fordetermining face image quality according to the present disclosure. Asshown in FIG. 3, the method for determining face image quality of thisembodiment includes:

302: a face detection bounding box in the image, key point coordinatesof the face determined according to the face detection bounding box, anda confidence score of the key point coordinates are obtained.

The confidence score of the key point coordinates is used for indicatingthe accuracy of the key point coordinates of the face, and the greaterthe numerical value of the confidence score is, the more accurate thekey point coordinates of the face are.

According to one or more embodiments of the present disclosure,operation 302 may be implemented through a pre-trained first neuralnetwork. After receiving an input image, the first neural networkoutputs the face detection bounding box, the key point coordinates ofthe face determined according to the face detection bounding box, andthe confidence score of the key point coordinates by performing facedetection and key point detection on the image. The confidence score ofthe key point coordinates may be determined by the first neural networkon the basis of the performance of the first neural network and the sizeof the face detection bounding box according to a preset mode. Thebetter the performance of the first neural network is, and the largerthe face detection bounding box is (i.e., the face image is relativelylarge, and the face is relatively clear), the higher the accuracy of thedetermined key point coordinates of the face is.

304: The pose angle information of the face is obtained according to thekey point coordinates of the face, and the size information of the faceis obtained according to the size of the face detection bounding box,where the pose angle information of the face includes a yaw angle and apitch angle of the face.

306: The score of the pose angle of the face is obtained according tothe pose angle information of the face, and the score of the face sizeis obtained according to the size information of the face.

According to one or more embodiments of the present disclosure, thescore of the pose angle of the face may be obtained in the followingmode:

according to the yaw angle and the pitch angle of the face, obtainingthe score Q_(yaw) of the yaw angle yaw of the face by calculation basedon

${Q_{yaw} = e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}},$

and obtaining the score Q_(pitch) of the pitch angle (“pitch”) of theface by calculation based on

$Q_{pitch} = {e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}.}$

According to one or more embodiments of the present disclosure, thescore of the face size may be obtained in the following mode: obtainingthe score of the face size on the basis of at least one of length,width, or area of the face detection bounding box, where the area of theface detection bounding box is obtained by the product of the length andthe width of face detection bounding box.

The length, width, and area of face detection bounding box correspond tothe size of the face image. Therefore, the score of the face size may bedetermined on the basis of at least one of length, width, or area of theface detection bounding box.

Further exemplarily, the obtaining the score of the face size on thebasis of at least one of length, width, or area of the face detectionbounding box, for examples, may be: selecting a smaller value min in thelength and the width of the face detection bounding box; and obtainingthe score Q_(rect) of the face size by calculation based on

$Q_{rect} = \frac{1}{1 + e^{\frac{{- 2^{*}}{({\min - 50})}}{75}}}$

according to the smaller value min in the length and the width.

The face size may be better determined by means of the smaller value inthe length and the width of the face detection bounding box, so that thescore of the face size is obtained by calculation on the basis of thesmaller value in the length and the width of the face detection boundingbox, and the face size may be reflected more objectively.

308: The pose angle of the face is corrected by using the confidencescore of the key point coordinates.

Exemplarily, by using the confidence score of the key point coordinates,the corrected score Q_(yaw) of the yaw angle and the corrected score Q_(pitch) of the pitch angle are obtained by calculation based on

$Q_{yaw} = {{a*e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}\mspace{14mu}{and}\mspace{14mu} Q_{pitch}} = {a*e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}}}$

respectively, wherein

$a = \left\{ {\begin{matrix}{Q\;{{align}\left( {{Q\;{align}} < 0.4} \right)}} \\{1\left( {{Q\;{align}} > 0.4} \right)}\end{matrix},} \right.$

and Qalign represents the confidence score of the key point coordinates.According to one or more embodiments of the present disclosure,operation 308 may be performed simultaneously with the operation ofobtaining the score of the face size, before or after the operation ofobtaining the score of the face size, and no execution time limit existsbetween the two operations.

310: The quality score of the face in the image is obtained according tothe corrected score of the pose angle of the face and the score of theface size.

When the key point coordinates of the face are inaccurate, the poseangle information of the face obtained based on the key pointcoordinates is also inaccurate. In order to solve the problem of theinaccuracy of evaluation of the pose angle information of the face dueto the inaccuracy of the key point coordinates, in the embodiments ofthe present disclosure, the score of the pose angle of the face obtainedby calculation is correspondingly corrected according to the confidencescore of the key point coordinates of the face, so as to eliminate theinaccuracy of evaluation of the pose angle information of the face dueto the inaccuracy of the key point coordinates, and the influence on thefinal determination of the result of the face image quality, and improvethe accuracy and reliability of determining the result of the face imagequality.

FIG. 4 is a flowchart of a specific application embodiment of a methodfor determining face image quality according to the present disclosure.As shown in FIG. 4, the method for determining face image quality ofthis embodiment includes:

402: face detection is performed on the image to obtain a face detectionbounding box.

404: Key point positioning is performed on the face in the facedetection bounding box to obtain key point coordinates of the face and aconfidence score of the key point coordinates. The confidence score ofthe key point coordinates is used for indicating the accuracy of the keypoint coordinates of the face.

According to one or more embodiments of the present disclosure,operations 402-404 may be implemented through a pre-trained first neuralnetwork. After receiving an input image, the first neural networkoutputs the face detection bounding box, the key point coordinates ofthe face, and the confidence score of the key point coordinates byperforming face detection and key point detection on the image. Theconfidence score of the key point coordinates may be determined by thefirst neural network on the basis of the performance of the first neuralnetwork and the size of the face detection bounding box according to apreset mode. The better the performance of the first neural network is,and the larger the face detection bounding box is (i.e., the face imageis relatively large, and the face is relatively clear), the higher theaccuracy of the determined key point coordinates of the face is.

Then, operations 406 and 406′ are executed respectively.

406: The pose angle information of the face is obtained according to thekey point coordinates of the face, where the pose angle information ofthe face includes a yaw angle and a pitch angle of the face. Accordingto one or more embodiments of the present disclosure, operation 406 maybe implemented by a pre-trained second neural network. After receivingthe key point coordinates of the face, the second neural network outputsthe yaw angle and the pitch angle of the face by calculating the keypoint coordinates of the face.

408: According to the yaw angle and the pitch angle of the face, thescore Q_(yaw) of the yaw angle yaw of the face is obtained bycalculation based on

${Q_{yaw} = e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}},$

and the score Q_(pitch) of the pitch angle (“pitch”) of the face isobtained by calculation based on

$Q_{pitch} = {e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}.}$

410: The score of the pose angle of the face is corrected by using theconfidence score of the key point coordinates.

Exemplarily, by using the confidence score of the key point coordinates,the corrected score Q_(yaw) of the yaw angle and the corrected scoreQ_(pitch) of the pitch angle are obtained by calculations based on

$Q_{yaw} = {{a*e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}\mspace{14mu}{and}\mspace{14mu} Q_{pitch}} = {a*e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}}}$

respectively, where

$a = \left\{ {\begin{matrix}{Q\;{{align}\left( {{Q\;{align}} < 0.4} \right)}} \\{1\left( {{Q\;{align}} > 0.4} \right)}\end{matrix},} \right.$

and Qalign represents the confidence score of the key point coordinates.

Then, operation 412 is executed.

406′: The size of the face detection bounding box is obtained, includinglength and width of the face detection bounding box.

408′: A smaller value min in the length and the width of the facedetection bounding box is selected.

410′: The score Q_(rect) of the face size is obtained by calculationbased on

$Q_{rect} = \frac{1}{1 + e^{\frac{{- 2^{*}}{({\min - 50})}}{75}}}$

according to the smaller value min in the length and the width.

No sequential relation limit of execution time exists between operations406-410 and operations 406′-410′, which may be executed in any time andsequence.

412: The quality of the face in the image is obtained by calculationaccording to the corrected score of the yaw angle and its weight, thecorrected score of the pitch angle and its weight, and the score of theface size and its weight.

For example, the quality of the face in the image may be obtained bycalculation based on Q_(=w1)*Q_(yaw+w2)*Q_(pitch+3w)*Q_(rect).

Q is the quality of the face in the image, Q_(yaw) represents thecorrected score of the yaw angle (“yaw”), Q _(pitch) represents thecorrected score of the pitch angle (“pitch”), and Q_(rect) representsthe score of the face size. w1, w2, and w3 respectively represent theweight of the score of the yaw angle, the weight of the score of thepitch angle, and the weight of the score of the face size. Generally,the yaw angle has the greatest influence on the accuracy of the facerecognition result, and the value of w1 may be set to 0.6; both theweight w2 of the score of the pitch angle and the weight w3 of the scoreof the size of the face may be set to 0.2, and may also be adjustedaccording to actual conditions.

Further, the foregoing embodiments of the method for determining faceimage quality according to the present disclosure are executed for anyone of multiple images of the same face respectively, so as to obtainthe quality score of the face in the multiple images. Yet anotherembodiment of the method for determining face image quality according tothe present disclosure may further include: selecting, according to thequality information of the face in the multiple images, selecting atleast one image with high face quality for face detection.

On the basis of the embodiments, the images with poor face quality areremoved, and the images with high face quality are selected for facedetection and recognition. Because the selected images with high facequality have a high face recognition rate, the accuracy of facerecognition may be improved, the operation data volume of the facerecognition may be reduced, and the face recognition speed of a validimage may be improved.

FIG. 5 is a schematic structural diagram of one embodiment of anapparatus for determining face image quality according to the presentdisclosure. The apparatus for determining face image quality of thisembodiment may be configured to implement the foregoing embodiments ofthe method for determining face image quality according to the presentdisclosure. As shown in FIG. 5, the apparatus for determining face imagequality of this embodiment includes: a first obtaining module 502 and asecond obtaining module 504.

The first obtaining module 502 is configured to obtain pose angleinformation and size information of a face in an image;

The second obtaining module 504 is configured to obtain qualityinformation of the face in the image on the basis of the pose angleinformation and the size information of the face.

On the basis of the apparatus for determining face image qualityprovided according to the foregoing embodiments of the presentdisclosure, the face image quality is evaluated on the basis of keyfactors affecting the face recognition result (for example, facedefinition, face size, and whether the face is front-facing), andindexes for evaluating the key factors affecting the face recognitionresult are obtained: a pose angle of the face and a face size. Thequality of the face in the image is determined according to the poseangle of the face and the face size. According to the technicalsolutions for determining face image quality in the embodiments of thepresent disclosure, the face image quality may be objectively evaluated,and the accuracy rate of the evaluation result of the face image qualityis high; in addition, according to the embodiments of the presentdisclosure, by obtaining the size information of the face to reflect theface definition affecting the face recognition result instead ofdirectly obtaining the face definition in the image, as compared withthe directly obtaining the face definition in the image, the methodfacilitates improving the operation efficiency and increasing thereal-time performance of the face quality evaluation.

FIG. 6 is a schematic structural diagram of another embodiment of anapparatus for determining face image quality according to the presentdisclosure. As shown in FIG. 6, in this embodiment, the first obtainingmodule 502 specifically includes: a face detection sub-module 602, a keypoint detection sub-module 604, and a first obtaining sub-module 606.

The face detection sub-module 602 is configured to obtain a facedetection bounding box in the image, where the face detection boundingbox is configured to determine the face in the image. According to oneor more embodiments of the present disclosure, the face detectionsub-module 602 may be configured to perform face detection on the imageto obtain the face detection bounding box.

The key point detection sub-module 604 is configured to obtain key pointcoordinates of the face determined according to the face detectionbounding box. According to one or more embodiments of the presentdisclosure, the key point detection sub-module 604 may be configured toperform key point positioning on the face image determined according tothe face detection bounding box to obtain the key point coordinates ofthe face.

The first obtaining sub-module 606 is configured to obtain pose angleinformation of the face according to the key point coordinates of theface, where the pose angle information of the face includes a yaw angleand a pitch angle of the face, and to obtain size information of theface according to the size of the face detection bounding box, where thesize of the face detection bounding box includes length and/or width ofthe face detection bounding box.

In addition, in the embodiments of the apparatus for determining faceimage quality, the face detection sub-module 602 is configured toperform face detection on an image to obtain the face detection boundingbox, where the face detection bounding box includes the image of theface, called: a face image. Accordingly, the key point detectionsub-module 604 is configured to perform key point positioning on theface image determined according to the face detection bounding box toobtain the key point coordinates of the face.

In addition, with reference to FIG. 6 again, in still another embodimentof the apparatus for determining face image quality according to thepresent disclosure, the second obtaining module 504 may include a secondobtaining sub-module 608, a third obtaining sub-module 610, and a fourthobtaining sub-module 612.

The second obtaining sub-module 608 is configured to obtain the score ofthe pose angle of the face according to the pose angle information ofthe face.

According to one or more embodiments of the present disclosure, thesecond obtaining sub-module 608 is configured to obtain, according tothe yaw angle and the pitch angle of the face, the score Q_(yaw) of theyaw angle (“yaw”) of the face by calculation based on

${Q_{yaw} = e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}},$

and the score Q _(pitch) of the pitch angle (“pitch”) of the face bycalculation based on

$Q_{pitch} = {e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}.}$

Further exemplarily, the second obtaining module 608 may obtain thescore of the face size on the basis of at least one of length, width, orarea of the face detection bounding box in the following mode: selectinga smaller value min in the length and the width of the face detectionbounding box; and obtaining the score Q_(rect) of the face size bycalculation based on

$Q_{rect} = \frac{1}{1 + e^{\frac{{- 2^{*}}{({\min - 50})}}{75}}}$

according to the smaller value min in the length and the width.

The third obtaining sub-module 610 is configured to obtain the score ofthe face size according to the size information of the face.

According to one or more embodiments of the present disclosure, thethird obtaining sub-module 610 is configured to obtain the score of theface size on the basis of at least one of length, width, or area of theface detection bounding box, where the area of the face detectionbounding box is obtained by the product of the length and the width offace detection bounding box.

The fourth obtaining sub-module 612 is configured to obtain the qualityscore of the face in the image according to the score of the pose angleof the face and the score of the face size. According to one or moreembodiments of the present disclosure, the fourth obtaining sub-module612 is configured to obtain the quality of the face in the image bycalculation according to the score of the yaw angle and its weight, thescore of the pitch angle and its weight, and the score of the face sizeand its weight. In an actual application, because the yaw angle of theface has the greatest influence on the accuracy of the face recognitionresult, the weight of the score of the yaw angle may be set to begreater than the weight of the score of the pitch angle and the weightof the score of the face size.

FIG. 7 is a schematic structural diagram of still another embodiment ofan apparatus for determining face image quality according to the presentdisclosure. As shown in FIG. 7, compared with the apparatus fordetermining face image quality according to the foregoing embodiments ofthe present disclosure, the apparatus for determining face image qualityin this embodiment further includes: a fourth obtaining module 506 and acorrection module 508.

The fourth obtaining module 506 is configured to obtain the confidencescore of the key point coordinates, where the confidence score of thekey point coordinates is configured to represent an accuracy rate of thekey point coordinates of the face.

Exemplarily, the fourth obtaining module 506 may be integrated with thekey point detection sub-module 604, and the two may be implemented bymeans of a neural network.

The correction module 508 is configured to correct, by using theconfidence score of the key point coordinates, the score of pose angleof the face obtained by the second obtaining sub-module 608.

According to one or more embodiments of the present disclosure, thecorrection module 508 is configured to obtain, by using the confidencescore of the key point coordinates, the corrected score Q_(yaw) of theyaw angle and the corrected score Q_(pitch) of the pitch angle bycalculation based on

$Q_{yaw} = {{a*e^{{- 1}0^{*}\frac{{yaw}^{2}}{90^{*}90}}\mspace{14mu}{and}\mspace{14mu} Q_{pitch}} = {a*e^{{- 1}0^{*}\frac{{pitch}^{2}}{90^{*}90}}}}$

respectively, where

$a = \left\{ {\begin{matrix}{Q\;{{align}\left( {{Q\;{align}} < 0.4} \right)}} \\{1\left( {{Q\;{align}} > 0.4} \right)}\end{matrix},} \right.$

and Qalign represents the confidence score of the key point coordinates.

Accordingly, in the embodiments, the fourth obtaining sub-module 612 isconfigured to obtain the quality of the face in the image according tothe corrected score of the pose angle of the face and the score of theface size.

The embodiments of the present disclosure further provide an electronicdevice, including the apparatus for determining face image qualityaccording to any one of the foregoing embodiments of the presentdisclosure. By obtaining indexes for evaluating the key factorsaffecting the face recognition result: a pose angle of the face and facesize, and evaluating the face image quality according to the pose angleinformation and the size information of the face, the face image qualityis objectively evaluated, and the accuracy rate of the evaluation resultof the face image quality is high; in addition, according to theembodiments of the present disclosure, by obtaining the size informationof the face to reflect the face definition affecting the facerecognition result instead of directly obtaining the face definition inthe image, as compared with the directly obtaining the face definitionin the image, the device facilitates improving the operation efficiencyand increasing the real-time performance of the face quality evaluation.

Further, the embodiments of the electronic device further include aselection module and a face detection module.

The selection module is configured to select, according to qualityinformation of a face in multiple images output by the apparatus fordetermining face image quality, at least one image with high facequality;

the face detection module is configured to perform face detection on theselected at least one image.

On the basis of the embodiments, the images with poor face quality areremoved, and the images with high face quality are selected for facedetection and recognition. Because the selected images with high facequality have a high face recognition rate, the accuracy rate of facerecognition may be improved, the operation data volume of facerecognition may be reduced, and the face recognition speed of a validimage may be improved.

The embodiments of the present disclosure further provide anotherelectronic device, including: a memory, configured to store executableinstructions; and a processor, configured to communicate with the memoryto execute the executable instructions so as to complete operations ofthe method for determining face image quality according to any one ofthe foregoing embodiments of the present disclosure.

The electronic device according to the foregoing embodiments of thepresent disclosure, for example, may be a mobile terminal, a PC, atablet computer, a server, and the like.

The embodiments of the present disclosure further provide a computerstorage medium, which is configured to store computer-readableinstructions. When the instructions are executed, the operations of themethod for determining face image quality according to any one of theforegoing embodiments of the present disclosure are implemented.

FIG. 8 is a schematic structural diagram of one embodiment of anelectronic device according to the present disclosure. With reference toFIG. 8 below, a schematic structural diagram of an electronic devicesuitable for implementing the terminal device or the server of theembodiments of the present disclosure is shown. As shown in FIG. 8, theelectronic device includes one or more processors, and a communicationpart, etc. The one or more processors are, for example, one or moreCentral Processing Units (CPUs) 801 and/or one or more GraphicProcessing Units (GPUs) 813, and the processors may execute appropriateactions and processing according to executable instructions stored in aRead-Only Memory (ROM) 802 or executable instructions loaded from astorage section 808 to a Random Access Memory (RAM) 803. Thecommunication part 812 may include, but is not limited to, a networkcard. The network card may include, but is not limited to, an InfiniBand(IB) network card.

The processor may communicate with the ROM 802 and/or the RAM 803 toexecute executable instructions, is connected to the communication part812 by means of a bus 804, and communicates with other target devices bymeans of the communication part 812, so as to complete correspondingoperations of any of the methods provided by the embodiments of thepresent disclosure, for example, obtaining pose angle information andsize information of a face in an image, and obtaining qualityinformation of the face in the image on the basis of the pose angleinformation and the size information of the face.

In addition, the RAM 803 may further store various programs and datarequired during an operation of the apparatus. The CPU 801, the ROM 802,and the RAM 803 are connected to each other via the bus 804. In thepresence of the RAM 803, the ROM 802 is an optional module. The RAM 803stores executable instructions, or writes executable instructions to theROM 802 during running. The executable instructions cause the CPU 801 toexecute the operations of the communication method. An Input/Output(I/O) interface 805 is also connected to the bus 804. The communicationpart 812 may be integrated, or may be set as having multiple sub-modules(for example, multiple IB network cards) connected to the bus.

The following components are connected to the I/O interface 805: aninput section 806 including a keyboard, a mouse and the like; an outputsection 807 including a Cathode-Ray Tube (CRT), a Liquid Crystal Display(LCD), a speaker and the like; a storage section 808 including a harddisk and the like; and a communication section 809 of a networkinterface card including an LAN card, a modem and the like. Thecommunication section 809 executes communication processing through anetwork such as the Internet. A drive 810 is also connected to the I/Ointerface 805 according to requirements. A removable medium 811 such asa magnetic disk, an optical disk, a magneto-optical disk, asemiconductor memory or the like is mounted on the drive 810 accordingto requirements, so that a computer program read from the removablemedium may be installed on the storage section 808 according torequirements.

It should be noted that, the architecture shown in FIG. 8 is merely anoptional implementation mode. During specific practice, the number andtypes of the components in FIG. 8 may be selected, decreased, increased,or replaced according to actual needs. Different functional componentsmay be separated or integrated or the like. For example, the GPU 813 andthe CPU 801 may be separated, or the GPU 813 may be integrated on theCPU 801, and the communication part may be separated from or integratedon the CPU 801 or the GPU 813 or the like. These alternativeimplementations all fall within the scope of protection of the presentdisclosure.

Particularly, a process described above with reference to a flowchartaccording to the embodiments of the present disclosure may beimplemented as a computer software program. For example, the embodimentsof the present disclosure include a computer program product. Thecomputer program product includes a computer program tangibly includedin a machine-readable medium. The computer program includes a programcode for executing a method shown in the flowchart. The program code mayinclude corresponding instructions for correspondingly executing theoperations of the method provided by the embodiments of the presentdisclosure, for example, an instruction for obtaining pose angleinformation and size information of a face in an image, and aninstruction for obtaining quality information of the face in the imageon the basis of the pose angle information and the size information ofthe face. In such embodiments, the computer program may be downloadedand installed from the network through the communication part 809,and/or is installed from the removable medium 811. When executed by theCPU 801, the computer program executes the foregoing functions definedin the method of the present disclosure.

The embodiments of the present disclosure may be optionally applied to:residential area monitoring or security monitoring fields, a capturemachine, or products related to face recognition. Face detection isperformed on an image collected by a camera (i.e., the image in theembodiments of the present disclosure), and a face image is recognized.In order to improve the accuracy rate of face recognition, reduce thefalse recognition rate and the missing recognition rate, and avoidunnecessary recognition, it is necessary to first provide images to anapparatus or a device for determining face image quality, the images arescreened and filtered, so as to obtain high-quality face images. Byevaluating the face image quality, the images having a big side face ordeeply head-down or having an extremely low face pixel (i.e., anextremely small face size) may be screened out due to difficulty inaccurate recognition. Through the methods, apparatuses, or devices fordetermining face image quality according to the embodiments of thepresent disclosure, the quality of a face in various images may beobtained, and the images having low face quality and unsuitable for facerecognition are effectively filtered out, so as to reduce the number offace recognitions and improve the face recognition efficiency. In ascene where the embodiments of the present disclosure are applied to anembedded device for face recognition which is time-consuming, the effectis more obvious.

The embodiments of the present disclosure have at least the followingbeneficial technical effects: according to the embodiments of thepresent disclosure, face image requirements facilitating facerecognition are fully considered, a pose angle of a face is evaluatedand evaluation indexes are designed in combination of the face size, theface image quality is comprehensively evaluated based on the combinationof a yaw angle and a pitch angle of the face and the face size, andconditions which may cause inaccurate evaluation of the pose angle ofthe face are corrected. The method is high in real-time performance andeasy to apply, and the accuracy and reliability of the evaluation methodare ensured. By obtaining the size information of the face to reflectthe face definition affecting the face recognition result instead ofdirectly obtaining the face definition in the image, as compared withthe directly obtaining the face definition in the image, the methodfacilitates improving the operation efficiency and increasing thereal-time performance of the face quality evaluation. By removing theimages with poor face quality and selecting the images with high facequality for face detection and recognition, the accuracy rate of facerecognition may be improved, the operation data volume of facerecognition may be reduced, and the face recognition speed of a validimage may be improved.

A person of ordinary skill in the art may understand that: all or someoperations for implementing the foregoing method embodiments areachieved by a program by instructing related hardware; the foregoingprogram may be stored in a computer-readable storage medium; when theprogram is executed, operations including the foregoing methodembodiments are executed. Moreover, the foregoing storage mediumincludes various media capable of storing program codes, such as a ROM,a RAM, a magnetic disk, or an optical disk.

Various embodiments in this description are all described in aprogressive manner, for same or similar parts in the embodiments, referto these embodiments, and each embodiment focuses on a difference fromother embodiments. The system embodiments correspond to the methodembodiments substantially and therefore are only described briefly, andfor the associated part, refer to the descriptions of the methodembodiments.

The methods and the apparatuses of the present disclosure may beimplemented in many manners. For example, the methods and apparatuses ofthe present disclosure may be implemented by using software, hardware,firmware, or any combination of software, hardware, and firmware. Unlessotherwise specially stated, the foregoing sequences of operations of themethods are merely for description, and are not intended to limit theoperations of the methods of the present disclosure. In addition, insome embodiments, the present disclosure may be implemented as programsrecorded in a recording medium. The programs include machine-readableinstructions for implementing the methods according to the presentdisclosure. Therefore, the present disclosure further covers therecording medium storing the programs for performing the methodsaccording to the present disclosure.

The descriptions of the present disclosure are provided for the purposeof examples and description, and are not intended to be exhaustive orlimit the present disclosure to the disclosed form. Many modificationsand changes are obvious to a person of ordinary skill in the art. Theembodiments are selected and described to better describe a principleand an actual application of the present disclosure, and to make aperson of ordinary skill in the art understand the present disclosure,so as to design various embodiments with various modificationsapplicable to particular use.

What is claimed is:
 1. A method for determining face image quality,comprising: obtaining at least one of pose angle information of a facein an image or size information of the face; and obtaining qualityinformation of the face in the image on the basis of at least one of thepose angle information of the face or the size information of the face,wherein the obtaining quality information of the face in the image onthe basis of at least one of the pose angle information of the face orthe size information of the face comprises: obtaining, according to thepose angle information of the face, a score of a pose angle of the face;obtaining, according to the size information of the face, a score of aface size; and obtaining, according to the score of the pose angle ofthe face and the score of the face size, a quality score of the face inthe image, wherein the obtaining, according to the size information ofthe face, a score of a face size comprises: obtaining the score of theface size on the basis of at least one of length, width, or area of theface detection bounding box, the area of the face detection bounding boxbeing obtained by a product of the length and width of face detectionbounding box, wherein the obtaining the score of the face size on thebasis of at least one of length, width, or area of the face detectionbounding box comprises: selecting a smaller value min in the length andwidth of the face detection bounding box; and obtaining, according tothe smaller value min, the score Q_(rect) of the face size bycalculation.
 2. The method according to claim 1, wherein the obtainingthe pose angle information of the face in the image comprises: obtaininga face detection bounding box in the image and key point coordinates ofthe face determined according to the face detection bounding box; andobtaining, according to the key point coordinates of the face, the poseangle information of the face, the pose angle information of the facecomprising a yaw angle of the face and a pitch angle of the face.
 3. Themethod according to claim 2, wherein the obtaining a face detectionbounding box in the image and key point coordinates of the facedetermined according to the face detection bounding box comprises:performing face detection on the image; obtaining the face detectionbounding box; and performing key point positioning on the face in theface detection bounding box; obtaining the key point coordinates of theface.
 4. The method according to claim 2, wherein the obtaining the sizeinformation of the face comprises: obtaining, according to a size of theface detection bounding box, the size information of the face, the sizeof the face detection bounding box comprising at least one of length ofthe face detection bounding box or width of the face detection boundingbox.
 5. The method according to claim 1, wherein the obtaining a scoreof a pose angle of the face according to the pose angle information ofthe face comprises: obtaining, according to the yaw angle and pitchangle of the face, a score Q_(yaw) of the yaw angle yaw of the face anda score Q_(pitch) of the pitch angle (“pitch”) of the face bycalculation.
 6. The method according to claim 1, wherein the obtaining,according to the score of the pose angle of the face and the score ofthe face size, a quality score of the face in the image comprises:obtaining, according to a score of the yaw angle and a weight of thescore of the yaw angle, a score of the pitch angle and a weight of thescore of the pitch angle, and the score of the face size and a weight ofthe score of the face size, the quality score of the face in the imageby calculation.
 7. The method according to claim 6, wherein the weightof the score of the yaw angle is greater than the weight of the score ofthe pitch angle and the weight of the score of the face size.
 8. Themethod according to claim 2, wherein the method further comprises:obtaining a confidence score of the key point coordinates of the face,the confidence score of the key point coordinates being used forindicating accuracy of the key point coordinates of the face; afterobtaining the score of the pose angle of the face, the method furthercomprises: correcting the score of the pose angle of the face by usingthe confidence score of the key point coordinates; and the obtaining,according to the score of the pose angle of the face and the score ofthe face size, a quality score of the face in the image comprises:obtaining, according to the corrected score of the pose angle of theface and the score of the face size, the quality score of the face inthe image.
 9. The method according to claim 8, wherein the correctingthe score of the pose angle of the face by using the confidence score ofthe key point coordinates comprises: determining a correction parametera for the score Q_(yaw) of the yaw angle (“yaw”) of the face and thescore Q_(pitch) of the pitch angle (“pitch”) of the face by using theconfidence score of the key point coordinates, and respectivelycalculating product of the correction parameter a and the Q_(yaw) aswell as product of the correction parameter a and the Q_(pitch), theproduct of the correction parameter a and the Q_(yaw) being used as acorrected score of the yaw angle and the product of the correctionparameter a and the Q_(pitch) being used as a corrected score of thepitch angle; wherein in the case that the confidence score of the keypoint coordinates is smaller than a predetermined value, a value of thea is a first value, in the case that the confidence score of the keypoint coordinates is greater than or equal to the predetermined value,the value of the a is a second value, and the first value is smallerthan the second value.
 10. The method according to claim 1, wherein theobtaining at least one of pose angle information of a face in an imageor size information of the face and obtaining quality information of theface in the image on the basis of at least one of the pose angleinformation of the face or the size information of the face comprises:obtaining at least one of: pose angle information of a face in at leastone image in multiple images or size information of the face; andobtaining quality information of the face in the at least one image inmultiple images on the basis of at least one of the pose angleinformation of the face or the size information of the face. the methodfurther comprises: selecting, according to the quality information ofthe face in the at least one image in multiple images, at least oneimage with high face quality for face detection.
 11. An electronicdevice, comprising: a processor; and a memory for storing instructionsexecutable by the processor; wherein execution of the instructions bythe processor causes the processor to perform: obtaining at least one ofpose angle information of a face in an image or size information of theface; and obtaining quality information of the face in the image on thebasis of at least one of the pose angle information of the face or thesize information of the face, wherein the obtaining quality informationof the face in the image on the basis of at least one of the pose angleinformation of the face or the size information of the face comprises:obtaining, according to the pose angle information of the face, a scoreof a pose angle of the face; obtaining, according to the sizeinformation of the face, a score of a face size; and obtaining,according to the score of the pose angle of the face and the score ofthe face size, a quality score of the face in the image, wherein theobtaining, according to the size information of the face, a score of aface size comprises: obtaining the score of the face size on the basisof at least one of length, width, or area of the face detection boundingbox, the area of the face detection bounding box being obtained by aproduct of the length and width of face detection bounding box, whereinthe obtaining the score of the face size on the basis of at least one oflength, width, or area of the face detection bounding box comprises:selecting a smaller value min in the length and width of the facedetection bounding box; and obtaining, according to the smaller valuemin, the score Q_(rect) of the face size by calculation.
 12. Theelectronic device according to claim 11, wherein the obtaining the poseangle information of the face in the image comprises: obtaining a facedetection bounding box in the image and key point coordinates of theface determined according to the face detection bounding box; andobtaining, according to the key point coordinates of the face, the poseangle information of the face, the pose angle information of the facecomprising a yaw angle of the face and a pitch angle of the face;wherein the obtaining the size information of the face comprises:obtaining, according to a size of the face detection bounding box, thesize information of the face, the size of the face detection boundingbox comprising at least one of length of the face detection bounding boxor width of the face detection bounding box.
 13. The electronic deviceaccording to claim 12, wherein the obtaining a face detection boundingbox in the image and key point coordinates of the face determinedaccording to the face detection bounding box comprises: performing facedetection on the image; obtaining the face detection bounding box; andperforming key point positioning on the face in the face detectionbounding box; obtaining the key point coordinates of the face.
 14. Theelectronic device according to claim 11, wherein the obtaining a scoreof a pose angle of the face according to the pose angle information ofthe face comprises: obtaining, according to the yaw angle and pitchangle of the face, a score Q_(yaw) of the yaw angle yaw of the face anda score Q_(pitch) of the pitch angle (“pitch”) of the face bycalculation.
 15. The electronic device according to claim 11, whereinthe obtaining, according to the score of the pose angle of the face andthe score of the face size, a quality score of the face in the imagecomprises: obtaining, according to a score of the yaw angle and a weightof the score of the yaw angle, a score of the pitch angle and a weightof the score of the pitch angle, and the score of the face size and aweight of the score of the face size, the quality score of the face inthe image by calculation.
 16. The electronic device according to claim15, wherein the weight of the score of the yaw angle is greater than theweight of the score of the pitch angle and the weight of the score ofthe face size.
 17. The electronic device according to claim 11, whereinexecution of the instructions by the processor further causes theprocessor to perform: obtaining a confidence score of key pointcoordinates of the face, the confidence score of the key pointcoordinates being used for indicating accuracy of the key pointcoordinates of the face; after obtaining the score of the pose angle ofthe face, execution of the instructions by the processor causes theprocessor to further perform: correcting the score of the pose angle ofthe face by using the confidence score of the key point coordinates; andthe obtaining, according to the score of the pose angle of the face andthe score of the face size, a quality score of the face in the imagecomprises: obtaining, according to the corrected score of the pose angleof the face and the score of the face size, the quality score of theface in the image.
 18. The electronic device according to claim 17,wherein the correcting the score of the pose angle of the face by usingthe confidence score of the key point coordinates comprises: determininga correction parameter a for the score Q_(yaw) of the yaw angle(“yaw”)of the face and the score Q_(pitch) of the pitch angle (“pitch”)of the face by using the confidence score of the key point coordinates,and respectively calculating product of the correction parameter a andthe Q_(yaw) as well as product of the correction parameter a and theQ_(pitch), the product of the correction parameter a and the Q_(yaw)being used as a corrected score of the yaw angle and the product of thecorrection parameter a and the Q_(pitch) being used as a corrected scoreof the pitch angle; wherein in the case that the confidence score of thekey point coordinates is smaller than a predetermined value, a value ofthe a is a first value, in the case that the confidence score of the keypoint coordinates is greater than or equal to the predetermined value,the value of the a is a second value, and the first value is smallerthan the second value.
 19. The electronic device according to claim 11,wherein the obtaining at least one of pose angle information of a facein an image or size information of the face and obtaining qualityinformation of the face in the image on the basis of at least one of thepose angle information of the face or the size information of the facecomprises: obtaining at least one of: pose angle information of a facein at least one image in multiple images or size information of theface; and obtaining quality information of the face in the at least oneimage in multiple images on the basis of at least one of the pose angleinformation of the face or the size information of the face, whereinexecution of the instructions by the processor causes the processor tofurther perform: selecting, according to the quality information of theface in the at least one image in multiple images, at least one imagewith high face quality for face detection.
 20. A non-transitory computerstorage medium, configured to store computer-readable instructions,wherein execution of the instructions by the processor causes theprocessor to perform: obtaining at least one of pose angle informationof a face in an image or size information of the face; and obtainingquality information of the face in the image on the basis of at leastone of the pose angle information of the face or the size information ofthe face, wherein the obtaining quality information of the face in theimage on the basis of at least one of the pose angle information of theface or the size information of the face comprises: obtaining, accordingto the pose angle information of the face, a score of a pose angle ofthe face; obtaining, according to the size information of the face, ascore of a face size; and obtaining, according to the score of the poseangle of the face and the score of the face size, a quality score of theface in the image, wherein the obtaining, according to the sizeinformation of the face, a score of a face size comprises: obtaining thescore of the face size on the basis of at least one of length, width, orarea of the face detection bounding box, the area of the face detectionbounding box being obtained by a product of the length and width of facedetection bounding box, wherein the obtaining the score of the face sizeon the basis of at least one of length, width, or area of the facedetection bounding box comprises: selecting a smaller value min in thelength and width of the face detection bounding box; and obtaining,according to the smaller value min, the score Q_(rect) of the face sizeby calculation.